Koalas declared endangered in NSW, QLD and ACT
Climate change threatens koala habitat
NSW Koala Strategy: 7,000ha koala habitat to be protected on NSW private land by 2025
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 4.1.3
library(patchwork)
## Warning: package 'patchwork' was built under R version 4.1.3
library(sf)
## Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 7.2.1; sf_use_s2() is TRUE
library(geodata)
## Warning: package 'geodata' was built under R version 4.1.3
## Loading required package: terra
## Warning: package 'terra' was built under R version 4.1.3
## terra 1.7.18
##
## Attaching package: 'terra'
## The following object is masked from 'package:patchwork':
##
## area
plot_list <- readRDS('../plots/plot_list.rds')
shapes <- readRDS("../plots/shapes.rds")
aus_border <- shapes$aus_border
nsw_bbox <- shapes$nsw_bbox
nsw_lga_union <- shapes$nsw
bbox_buffer <- 1
wrap_elements(full=plot_list[[1]]$aus_plot)
Koala habitat: KITL index > 0.25
fcn_generate_plot <- function(i, plot_list) {
layout <- "
BBDDD
CCDDD
CCDDD"
plot_list[[i]]$covenanted_area_plot + plot_list[[i]]$year_trend_plot + plot_list[[i]]$prop_decisions_plot + plot_layout(design = layout)
}
fcn_generate_plot(1, plot_list)
CC: Enough protection under current conditions
fcn_generate_plot(2, plot_list)
RI: Robust but inflexible protection
fcn_generate_plot(3, plot_list)
F: Flexible protection – add new covenants in 2050
fcn_generate_plot(4, plot_list)
F+L: Flexible protection + learning and adapting to climate change
plot_list[[1]]$cost_plot
CC: $70M ($13-138M)
plot_list[[2]]$cost_plot
RI: $152M ($100-229M)
plot_list[[3]]$cost_plot
F: $84M ($36-161M) – 42% cost reduction
plot_list[[4]]$cost_plot
Flexible + Learning: $78M ($25-166M) – 44% cost reduction
plot_list_ns <- readRDS('../plots/plot_list_ns.rds')
bar_width <- 0.2
plot_list_ns[[1]]
Flexibility to delay investments only
plot_list_ns[[2]]
Flexibility to end interventions only
plot_list_ns[[3]]
Flexbility to delay and end investments
Strategic flexibility alters first-stage decisions
Flexibility mitigates trade-offs between maximizing conservation outcomes and managing risks
Flexibility to offer new covenants much more valuable than flexibility to end interventions
Thank you to co-authors: Brooke Williams, Carla Archibald, James Brazill-Boast, Michael Drielsma and Jonathan Rhodes
Sensitivities of estimates of the value of flexibility (A, E and A/E) under changes to the default parameters, showing sensitivities to a, sampling of 10 study populations randomly sampled with the stratified sampling approach (default index = 1), b, year covenant modification is allowed - t’ (default = 2050). c, koala landscape capacity indicator cut-off (default = 0.25) across a range of cut-offs where feasible solutions to the problem are found, and d. Amount of learning based on the number of climate scenarios decision-makers are uncertain about (1 being perfect certainty over climate change, default = 12), with “No Learning”, “Partial Learning” and “Full Learning” having parameters of 12, 3 and 1 respectively.
\[ \min_x E_{j \in J}\Big[\sum_i \sum_t c_{ijt}x_i + E_{S\in\xi}[Q(x,S)]\Big] \\ Q_j(x,S) := \min_{y,w} E_{s\in S}\Big[\sum_i \sum_{t\geq t'} c_{ijt}y_{is} - c_{ijt}w_{is}\Big] \\ s.t. \sum_i m_{ijts} x_i \geq K \quad \forall t=1,s\in S, j \in J \\ \sum_i m_{ijts}(x_i + y_{is} - w_{is} \geq K \quad \forall t \geq t', s \in S, j \in J \\ \sum_i m_{ijts} x_i \geq K \quad \forall t = 1, s \in S, j \in J \\ x_i + y_{is} \leq 1 \quad \forall i \in N, s \in S \\ x_i \geq w_{is} \quad \forall i \in N, s \in S \\ y_{is} = 0 \quad \forall i \in N, s \in S \\ w_{is} = 0 \quad \forall i \in N, s \in S \\ x,y,w \in [0,1] \]